2. UNIT-III: Basics Of Matrix
Definition: Matrix
A ractangular array of m rows and n columns, enclosed by brackets [ ] is
called a matrix of order 𝑚 × 𝑛. A matrix of order 3 × 3 is expressed as,
𝐴 = [
𝑎11 𝑎12 𝑎13
𝑎21 𝑎22 𝑎23
𝑎31 𝑎32 𝑎33
]
An element 𝑎𝑖𝑗 denotes, ith row and jth column
𝑎23 denotes, 2nd row and 3rd column.
Matrices are denoted by capital letters A,B,C,……..etc.
Types of Matrices:
1. Row Matrix:
A matrix having only single row is called row matrix. Its order is 1 × 𝑛.
For example,
𝐴 = [1 4]1×2
𝐴 = [2 4 −3]1×3
2. Column Matrix:
A matrix having single column is called column matrix. Its order is 𝑛 × 1.
For example,
𝐴 = [
2
1
]
2×1
𝐴 = [
4
−2
6
]
3×1
3. Square Matrix:
A matrx in which the number of rows is equal to number columns is called a
square matrix.
3. ∴ 𝑚 = 𝑛.
∴ No. of rows = No. of columns
For example,
𝐴 = [
1 3
−4 2
]
2×2
𝐴 = [
−3 2 1
2 3 1
3 1 −5
]
3×3
4. Null Matrix:
A matrix whose all elements are zero, is called null matrix.
For example,
𝐴 = [
0 0
0 0
]
𝐴 = [
0 0 0
0 0 0
0 0 0
]
5. Unit Matrix or Identity Matrix:
A matrix in which all the elements of its principal diagonal are unity(one)
and remaining elements are zero is called unit matrix.
It is denoted by I.
𝐼 = [
1 0
0 1
]
𝐼 = [
1 0 0
0 1 0
0 0 1
]
6. DiagonalMatrix:
A square matrix in which the elements on the principal diagonal are non zero
and all the other elements are zero, is called a diagonal matrix.
For example,
𝐴 = [
1 0
0 −5
]
4. 𝐴 = [
3 0 0
0 −1 0
0 0 2
]
7. ScalarMatrix:
A diagonal matrix in which all the elements of its principal diagonal are
equal is called scalar matrix.
For example,
𝐴 = [
5 0 0
0 5 0
0 0 5
]
𝐴 = [
−2 0
0 −2
]
8. Transpose Matrix:
For a given matrix A, if rows and column are interchanged ,the new matrix
obtained 𝐴’ is called transposeof a matrix.
Transposeof matrix A is denoted by 𝐴′ or 𝐴 𝑇
.
For example,
𝐴 = [
2 1 4
7 6 −3
4 1 0
] ∴ 𝐴′
= 𝐴 𝑇
= [
2 7 4
1 6 1
4 −3 0
]
( 𝐴′)′
= 𝐴
9. Symmetric Matrix:
A square matrix 𝐴 = [𝑎𝑖𝑗] is said to be symmetric, if 𝑎𝑖𝑗 = 𝑎𝑗𝑖 of each pair
(i, j) .
For Symmetric matrix 𝐴′
= 𝐴
For example,
𝐴 = [
4 2 0
2 −3 5
0 5 6
] ∴ 𝐴′
= [
4 2 0
2 −3 5
0 5 6
]
5. 10. Skew Symmetric Matrix:
A square matrix 𝐴 = [𝑎𝑖𝑗] is said to be skew symmetric if 𝑎𝑖𝑗 = −𝑎𝑗𝑖 for
each pair (i, j).
For Skew symmetric matrix 𝐴′
= −𝐴.
For example,
𝐴 = [
0 3 5
−3 0 4
−5 −4 0
] 𝐴′
= [
0 −3 −5
3 0 −4
5 4 0
]
11. Singular Matrix:
For a square matrix, if value of its determinant is zero, it is called singular
matrix.
For example,
𝐴 = |
6 2
9 3
| ∴ | 𝐴| = (6 × 3) − (9 × 2) = 18 − 18 = 0
𝐴 = |
1 2 3
4 1 5
3 6 9
| ∴ | 𝐴| = 1(9− 30) − 2(36− 15) + 3(24 − 3).
| 𝐴| = −21 − 42+ 63
| 𝐴| = 0
If | 𝐴| = 0 is called singular matrix.
If | 𝐴| ≠ 0 is called Non singular matrix.
12. Equal Matrix:
For two matrices, if their correspondingelements are equal, they are called
equal matrices.
For example,
𝐴 = [
2 3
−4 5
] 𝐵 = [
2 3
−4 5
] ∴ 𝐴 = 𝐵
13. Negative matrix:
6. For two matrices, if their correspondingelements are equal but opposite,
they are called Negative matrix.
For example,
𝐴 = [
1 −2
3 −5
] ∴ (−𝐴) = [
−1 2
−3 5
]
14. Orthogonalmatrix:
For square matrix A if Productof matirx A & its transpose matrix A’ (i.e.
AA’) is Identity matrix (I).
𝐴 = [
𝑐𝑜𝑠𝜃 −𝑠𝑖𝑛𝜃
𝑠𝑖𝑛𝜃 𝑐𝑜𝑠𝜃
]
𝐴′
= [
𝑐𝑜𝑠𝜃 𝑠𝑖𝑛𝜃
−𝑠𝑖𝑛𝜃 𝑐𝑜𝑠𝜃
]
𝐴𝐴′
= [
1 0
0 1
] = 𝐼
15. Upper Triangular Matrix:
For square matrix A if all the elements below the main diagonal are zero
then it is called a Upper Triangular Matrix.
For example,
𝐴 = [
1 4 5
0 −2 3
0 0 3
]
16. LowerTriangular Matrix:
For square matrix A if all the elements above the main diagonal are zero
then it is called a Lower Triangular Matrix.
For example,
𝐴 = [
1 0 0
4 2 0
3 −1 6
]
17. Trace ofMatrix:
7. For square matrix A sum of all diagonal elements are called trace of matrix
A.
For example,
𝐴 = [
−1 2 7
3 5 −8
1 2 7
]
𝑡𝑟( 𝐴) = −1 + 5 + 7 = 11
18. Idempotent Matrix:
Matrix A is said to be Idempotent matrix if matrix 𝐴 satisfy the equation
𝐴2
= 𝐴.
For example
𝐴 = [
2 −2 −4
−1 3 4
1 −2 −3
]
19. Involuntary Matrix:
Matrix A is said to be Involuntary matrix if matrix A satisfy the equation
𝐴2
= 𝐼. Since 𝐼2
= 𝐼 always. Therefore Unit matrix is involuntary.
20. Conjugate Matrix:
Let 𝐴 = [
1 + 𝑖 2 + 3𝑖 4
7 + 2𝑖 −𝑖 3 − 2𝑖
]
Conjugate of matrix A is 𝐴̅
𝐴̅ = [
1 − 𝑖 2 − 3𝑖 4
7 − 2𝑖 𝑖 3 + 2𝑖
]
Note: Transposeof the conjugate of a matrix A is denoted by 𝐴 𝜃
.
21.Unitary Matrix:
A square matrix A is said to be unitary if 𝐴 𝜃
𝐴 = 𝐼
For example,
8. 𝐴 = [
1+𝑖
2
−1+𝑖
2
−1−𝑖
2
1−𝑖
2
] , 𝐴 𝜃
= [
1−𝑖
2
1−𝑖
2
−1−𝑖
2
1+𝑖
2
], 𝐴. 𝐴 𝜃
= 𝐼
22. Hermitian Matrix:
A square matrix 𝐴 = (𝑎𝑖𝑗) is called Hermitian matrix, if every i-jth element
of A is equal to conjugate complex j-ith element of A.
In other words, 𝑎𝑖𝑗 = 𝑎𝑗𝑖̅̅̅̅
For example,
[
1 2 + 3𝑖 3 + 𝑖
2 − 3𝑖 2 1 − 2𝑖
3 − 𝑖 1 + 2𝑖 5
]
23. Skew Hermitian Matrix:
A square matrix 𝐴 = (𝑎𝑖𝑗) will be calledd a Skew Hermitian matrix if every
i-jth element of A is equal to negative conjugate complex of j-ith element of
A.
In other words , 𝑎𝑖𝑗 = −𝑎𝑗𝑖̅̅̅̅
For example, [
𝑖 2 − 3𝑖 4 + 5𝑖
−(2 + 3𝑖) 0 2𝑖
−(4 − 5𝑖) 2𝑖 −3𝑖
]
24. Minor:
The minor of an element in a third order determinant is a second order
determinant obtained by deletinng the row and column which contain that
element.
For example,
𝐴 = |
1 2 1
3 4 5
−1 1 2
|
Minor of element 2 = |
3 5
−1 2
|
9. Minor of element 3 = |
2 1
1 2
|
Operations On Matrices and it’s Properties:
Addition of Matrices :
If A and B be two matrices of the same order, then their sum, A+B is defined
as the matrix ,each element of which is the sum of the corresponding
elements of A and B.
Thus if 𝐴 = [
4 2 5
1 3 −6
] , 𝐵 = [
1 0 2
3 1 4
]
Then 𝐴 + 𝐵 = [
4 + 1 2 + 0 5 + 2
1 + 3 3 + 1 −6 + 4
]
𝐴 + 𝐵 = [
5 2 7
4 4 −2
].
If 𝐴 = [𝑎𝑖𝑗], 𝐵 = [𝑏𝑖𝑗] then 𝐴 + 𝐵 = [𝑎𝑖𝑗 + 𝑏𝑖𝑗]
Properties Of Matrix Addition:
Only matrices of the same order can be added or subtracted.
i. Commutative law: A + B = B + A
ii. Associative law: A + (B + C) = (A + B) + C
Subtraction of Matrices:
The difference of two matrices is a matrix, each element of which is obtained
by subtracting the elements of the second matrix from the
Corresponding element of the first.
𝐴 − 𝐵 = [𝑎𝑖𝑗 − 𝑎𝑗𝑖]
Thus [
8 6 4
1 2 0
] − [
3 5 1
7 6 2
]
= [
8 − 3 6 − 5 4 − 1
1 − 7 2 − 6 0 − 2
]
= [
5 1 3
−6 −4 −2
]
ScalarMultiple of a Matrix:
10. If a matrix is multiplied by a scalar quantity K, then each element is
multiplied by k,
i.e. 𝐴 = [
2 3 4
4 5 6
6 7 9
]
3𝐴 = 3[
3 × 2 3 × 3 3 × 4
3 × 4 3 × 5 3 × 6
3 × 6 3 × 7 3 × 9
] = [
6 9 12
12 15 18
18 21 27
]
Multiplication:
The productof two matrices A and B is only possible if the number of
columns in A is equal to the number of rows in B.
Let 𝐴 = [𝑎𝑖𝑗] be an 𝑚 × 𝑛 matrix and 𝐵 = [𝑏𝑖𝑗] be an 𝑛 × 𝑝 matrix. Then
the productAB of these matrices is an 𝑚 × 𝑝 matrix 𝐶 = [𝑐𝑖𝑗] where,
𝑐𝑖𝑗 = 𝑎𝑖1 𝑏1𝑗 + 𝑎𝑖2 𝑏2𝑗 + 𝑎𝑖3 𝑏3𝑗 + ⋯+ 𝑎𝑖𝑛 𝑏 𝑛𝑗
Properties of Matrix Multiplication:
a) Multiplication of matrix is not commutative.
𝑨𝑩 ≠ 𝑩𝑨
b) Matrix multiplication is associative , if conformability is assured.
𝑨( 𝑩𝑪) = ( 𝑨𝑩) 𝑪
c) Matrix multiplication is distributive with respectto addition.
𝑨( 𝑩 + 𝑪) = 𝑨𝑩 + 𝑨𝑪
d) Multiplcation of matrix A by unit matrix.
𝑨𝑰 = 𝑰𝑨 = 𝑨
e) Multiplicative inverse of a matrix exists if | 𝐴| ≠ 0.
𝑨. 𝑨−𝟏
= 𝑨−𝟏
. 𝑨 = 𝑰
f) If A is a square then 𝑨 × 𝑨 = 𝑨 𝟐
, 𝑨 × 𝑨 × 𝑨 = 𝑨 𝟑
g) 𝑨 𝟎
= 𝑰
h) 𝑰 𝒏
= 𝑰, where 𝑛 is positive integer.
17. 𝐶2 = −|
𝑎1 𝑎3
𝑏1 𝑏3
| = −𝑎1 𝑏3 + 𝑎3 𝑏1,
𝐶3 = |
𝑎1 𝑎2
𝑏1 𝑏2
| = 𝑎1 𝑏2 − 𝑎2 𝑏1,
Then the transposeof the matrix of co-factors
[
𝐴1 𝐵1 𝐶1
𝐴2 𝐵2 𝐶2
𝐴3 𝐵3 𝐶3
]
Is called the adjoint of the matrix 𝐴 and is written as 𝑎𝑑𝑗 𝐴.
Note:For 𝟐 × 𝟐 order matrix Adj A is defined as:
If 𝐴 = [
𝑎1 𝑎2
𝑏1 𝑏2
] then Adj A = [
𝑏2 −𝑎2
𝑏1 𝑎1
]
i.e. Change location of elelment of principal diagonal
and change sign of elements of subsidary diagonal.
Property of Adjoint Matrix:
The productof a matrix 𝐴 and its adjoint is equal to unit matrix multiplied by
the determinant 𝐴.
Example-1: If 𝑨 = [
𝟓 𝟐
𝟕 𝟑
] then find 𝑨−𝟏
.
Solution: Since here, given matrix 𝐴 is of the Order 2 × 2.
∴ 𝑎𝑑𝑗 𝐴 = [
3 −2
−7 5
]
Example-2: For matrix 𝑨 = [
𝟐 𝟏 𝟓
𝟎 𝟑 −𝟏
𝟐 𝟓 𝟎
] find co-factormatrix 𝒂𝒅𝒋 𝑨.
Solution: Let 𝐴 = [𝑎𝑖𝑗]
First we will find co-factors ofeach element.
𝐴11 = |
3 −1
5 0
| = 0 − (−5) = 5
𝐴12 = −|
0 −1
2 0
| = −[0 − (−2)] = −2
18. 𝐴13 = |
0 3
2 5
| = (0 − 6) = −6
𝐴21 = −|
1 5
5 0
| = −(0 − 25) = 25
𝐴22 = |
2 5
2 0
| = (0 − 10) = −10
𝐴23 = −|
2 1
2 5
| = −(10 − 2) = −8
𝐴31 = |
1 5
3 −1
| = (−1 − 15) = −16
𝐴32 = −|
2 5
0 −1
| = −(−2 − 0) = 2
𝐴33 = |
2 1
0 3
| = (6 − 0) = 6
∴ 𝑎𝑑𝑗 𝐴 = [
𝐴11 𝐴21 𝐴31
𝐴12 𝐴22 𝐴32
𝐴13 𝐴23 𝐴33
] = [
5 25 −16
−2 −10 2
−6 −8 6
]
Inverse of a Matrix:
If A and B are two square matrices of the same order, such that
𝐴𝐵 = 𝐵𝐴 = 𝐼
Then 𝐵 is called the inverse of 𝐴 i.e. 𝐵 = 𝐴−1
and 𝐴 is the invese of B.
Condition for a square matrix 𝐴 to possess aninverse is that matrix 𝐴 is
non-singular.
i.e. | 𝑨| ≠ 𝟎
If 𝐴 is square matrix and 𝐵 its inverse, then 𝐴𝐵 = 𝐼. Taking determinant of
both sides, we get
| 𝐴𝐵| = | 𝐼| 𝑜𝑟 | 𝐴|| 𝐵| = 𝐼
From this relation it is clear that | 𝐴| ≠ 0
i.e. the matrix 𝐴 is non singular.
To find the inverse matrix with the help of adjoint matrix: